A mix of traditional lectures and take-home exercises will enable the student to fully understand and practice the various mathematical techniques presented.
Module Aim:
To provide students with some mathematical techniques appropriate for computer science.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1
Apply the basic concepts of graph theory to analyse computer networks.
LO2
Solve various types of probability problems using the theory of probability distributions.
LO3
Explain and apply some numerical analysis techniques.
LO4
Demonstrate an understanding of number theory and its applications particularly basic cryptography.
LO5
Formulate statements using propositions and connectives.
LO6
Establish the validity of simple arguments using laws of reasoning
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
Module Content & Assessment
Indicative Content
Numerical Methods
Newton’s method, line and curve fitting, programming of techniques.
Graph Theory
Simple graphs, representing graphs, trees, connectivity, analysis of networks
Further Probability
Probability distributions, normal binomial and Poisson distributions.
Number Theory
Divisibility, Euclidean algorithm, linear congruences, applications to basic cryptography.